In the recent past, assisted driving systems for passenger vehicles became available that provide various support functions to the driver on the basis of processing sensor data of the vehicle's surroundings. The developments in this area lead to the rise of autonomous vehicle systems, allowing the operation of vehicles on typical roads without or with only little human intervention.
A portion of assisted driving systems as well as autonomous vehicle systems, as discussed above, use image data for at least part of their functionality, obtained from one or more cameras. For example, typical lane keeping features detect road markings from live camera images to allow determining an unintended lane departure.
For other applications, pre-processed image data is useful, e.g., images with annotations or “labels”. One example for such application is the automatic determination of whether a parking space in view of a camera is occupied or available.
While algorithms exist that allow automatic image annotation, reliable annotation of objects in vehicle image data is difficult due to the distortion caused by the typical angle of view of a vehicle camera, as well as the problem of moving objects, such as other vehicles, creating temporal occlusions, reflections, and other optical disturbances.
Accordingly, an object exists to improve the generation of vehicle image datasets, such as for, but not limited to, annotation or labeling applications.